Reviews: Learning Bayesian Networks with Low Rank Conditional Probability Tables

This paper presents a method for structural learning of a BN given observational data. The work is mainly theoretical, and for the proposal some assumptions are taken. A great effort is also given in presenting and develop theoretically the complexity of the algorithm. One of the key points in the proposed algorithm is the use of Fourier basis vectors (coefficients) and how they are applied in the compressed sensing step. I haven't checked thoroughly all the mathematical part, which is the core of the paper.